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How Kogenta creates insights by harnessing real world behaviour

Eating at a restaurant.
How Kogenta creates insights by harnessing real world behaviour
October 24, 2023

How Kogenta creates insights by harnessing real world behaviour

A Westfield Case Study – investigating footfall patterns, bringing context to geography 

Kogenta has revolutionised the utilisation of a wide range of unsystematic location, movement, and demographic data by converting it into easily accessible geo-contextual intelligence. This invaluable information empowers organisations to optimise business operations, enhance customer service, minimise potential risks, and boost profitability. Through the transformation of intricate data into actionable insights, we equip our clients with the knowledge to make informed decisions that drive their businesses forward.

The Kogenta taxonomy is vast and has been amalgamated through numerous data sources from location based collections. This helps us create Kogenta Contextual Indices (KCIs), which are proprietary indices created using a wide range of geo-contextual data uniquely combined with other verified data sources. They allow advertisers to gain valuable insights into user intent, interests, and demographics.

A unique addition to the taxonomy comes from the mobility data we receive from our partner, CKDelta. This data comes directly from the Three UK network, creating anonymised and aggregated data based off of cell tower trilateration. The data can be broken down into three separate offerings:

  • Point of Interest (POI) based footfall; movement into a POI
  • Origin-Destination footfall; journey patterns
  • Grid-based footfall; aggregated heatmaps

This blog focuses on Kogenta’s unique process to aggregate and use this movement data, alongside the KCIs, to create a new dynamic data offering providing valuable movement based insights – Kogenta’s Movement Enhanced Attributes (MEAs).

MEA: The Movement Enhanced Attributes fuse Telco grade mobility data from CKDelta/ThreeUK, with Kogenta’s best in class geo-contextual attributes, covering Demographic and Household Expenditure segments; all uniquely indexed against the national average.

The Westfield fan base

A case study is perhaps the most illuminating way to demonstrate the breadth, granularity and applicability of our MEAs. The extent to which we can examine this is emphasised through Kogenta’s bespoke polygon mapping layers, to the point where we can analyse specific POIs. Taking, for example, Westfield White City, a popular retail centre in West London, the largest in both the UK and Europe.

Well located in London, the centre is surrounded by an advanced public transport network. Although we would usually consider the local area when profiling the audience of a retail centre, when in London it is necessary to consider the fact that customers can venture from all over to commute to the centre. The map below uses the Three UK movement network, mapping the areas that show the highest footfall into the White City Westfield, highlighting the output areas that experience the most journeys into the Westfield.

Westfield London footfall.

Using these output areas we can then overlay the demographic attributes associated with the local postcodes. This leaves us with a general picture of the typical demographic that travels into Westfield.

UK residential demographics.

Examining this, we see some interesting socio-demographic attributes that over index for this audience. We see this targets a population of 110,262 people across 44,420 households. As expected, customers are typically quite well off, over indexing by 54% for household income – in essence over half of the households visiting Westfield take home over the national average. We also see an 82% over index for the Social Grade AB employees. The number of students and young professionals was also very high, with both the 18+ age group and the 20-44 age group over indexing heavily.

Kogenta’s MEA dataset provides the ability to profile visitors into postcode sectors, allowing us to examine the people that move into the area within a 500m radius of Westfield. Thus we can examine the typical, most average visitor into that area – a sample of these attributes and by how much they over index nicely depict this:

  • 62% for 25 to 44 year olds
  • 37% for ‘Restaurant Lovers’
  • 64% for ‘Cafe Culture’
  • 66% for ‘Fast Food Lovers’
  • 37% for ‘Bus Users’
  • 20% for ‘Beer Drinkers’

As similar to the previous portrait, the 25-44 age bracket significantly over indexes, as well as the Social Grade AB demographic. It’s also possible to see with our data that Westfield is a popular day out for coffee lovers, parents, pub goers, restaurant fans, veggies, techies, fashion enthusiasts and flat-dwellers. Phew! It’s definitely a good thing the shopping centre caters for all these people.

Kogenta offers Retail Data Analytics products designed for shopping malls and retailers who wish to understand their customer journeys better. The insights generated are immediately actionable and have been proven to make a measurable difference to revenue and customer satisfaction.

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